Development of cardiovascular and all-cause mortality risk prediction models for maintenance hemodialysis patients based on metabolomics.

Abstract

Introduction: The outcome of maintenance hemodialysis (MHD) remains poor, with cardiovascular death accounting for more than half of all-cause death cases. In this study, cardiovascular mortality and all-cause mortality prediction models were developed to investigate the predictive role of metabolites in MHD patients.

Methods: Clinical and metabolomics data of 135 hemodialysis patients from a single center were collected with a 6-year follow-up. Univariate Cox regression and random forest were respectively applied to preliminarily screen clinical and metabolomics characteristics, followed by multivariate Cox regression for identifying features predicting cardiovascular or all-cause mortality. Multivariate Cox proportional regression risk models were constructed using clinical, metabolomics, and combined features. Subgroup survival differences were compared via risk score stratification.

Results: The combined model showed significant superiority in predicting cardiovascular mortality (3-year AUC = 0.901, 5-year AUC = 0.876), surpassing the clinical-only model (0.868/0.826) and metabolomics-only model (0.659/0.641). For all-cause mortality, the combined model demonstrated modest improvement (0.859/0.834) but still outperformed the metabolomics model (0.534/0.653). Thirty 5-fold cross-validations confirmed stable performance. High-risk groups had significantly higher cumulative mortality than low-risk groups (p < 0.0001).

Conclusion: The metabolomics-alone model showed limited predictive performance, but its synergistic integration with clinical indicators further improved the predictive performance of mortality risk models, particularly for cardiovascular mortality.